English Language Proficiency and Content Assessment Performance: A Comparison of English Learners and Native English Speakers Achievement
Bibliographic record
Abstract
As a result of the accountability requirements established in Title III of the Elementary and Secondary Educational Act (ESEA) legislation, English Learners (ELs) are expected to make progress in both content area academic achievement and English Language Proficiency (ELP). In Tennessee ELs progress is measured by administering WIDA-Access to assess English language proficiency, and Tennessee Comprehensive Assessment Program (TCAP) standardized assessments to measure content academic achievement. The purpose of this study was to compare and analyze the performance levels of ELs who achieved the exit criteria on WIDA-Access state mandated English proficiency assessment and their subsequent performance on English Language Arts and Math TCAP assessments. Specifically, a comparison of EL’s achievement on TCAP was compared to the achievement on TCAP of non-ELs. Independent samples t-tests were performed on data from 302 elementary and middle school ELs and non-ELs that participated in WIDA-Access and TCAP assessments in 2015. Data analyses concluded that English Language Arts and Math TCAP scale scores were significantly different between ELs and non-ELs. Achievement levels in both English Language Arts TCAP and Math TCAP for ELs, who achieved the exit criteria on WIDA-Access, were lower than the achievement levels of non-ELs. Discussions of the findings in this study along with implications of using these assessments to measure ELs growth is provided in relation to the increased demands on measuring both the academic achievement and English language progress for ELs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".